Identifying Earth-impacting asteroids using an artificial neural network

Test Set ◽

Hazardous Object ◽

Artificial Neural ◽

By means of a fully connected artificial neural network, we identified asteroids with the potential to impact Earth. The resulting instrument, named the Hazardous Object Identifier (HOI), was trained on the basis of an artificial set of known impactors which were generated by launching objects from Earth’s surface and integrating them backward in time. HOI was able to identify 95.25% of the known impactors simulated that were present in the test set as potential impactors. In addition, HOI was able to identify 90.99% of the potentially hazardous objects identified by NASA, without being trained on them directly.

Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions

Journal of Thyroid Research ◽

10.1155/2020/5464787 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Christos Fragopoulos ◽

Abraham Pouliakis ◽

Christos Meristoudis ◽

Emmanouil Mastorakis ◽

Niki Margari ◽

...

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Histological Evaluation ◽

Image Analysis System ◽

Training Set ◽

Test Set ◽

Radial Basis ◽

Artificial Neural

Objective. This study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions. Study Design. The study was performed on 447 patients who had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid-based cytology, and the histological result was based on subsequent surgical samples. Each specimen was digitized; on these images, nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant. Results. The RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed. Conclusion. AI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images.

Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices

Sensors ◽

10.3390/s20123570 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3570

Author(s):

Daniele Marinucci ◽

Agnese Sbrollini ◽

Ilaria Marcantoni ◽

Micaela Morettini ◽

Cees A. Swenne ◽

...

Keyword(s):

Neural Network ◽

Atrial Fibrillation ◽

Operating Characteristic ◽

Area Under The Curve ◽

Portable Devices ◽

Promising Tool ◽

Artificial Neural ◽

Fully Connected ◽

Electrocardiogram Ecg

Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the “AF Classification from a Short Single Lead ECG Recording” database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1–93.0%), 90.2% (CI: 86.2–94.3%) and 90.8% (CI: 88.1–93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.

Power plant condenser performance forecasting using a non-fully connected artificial neural network

Energy ◽

10.1016/s0360-5442(00)00046-3 ◽

2001 ◽

Vol 26 (1) ◽

pp. 65-79 ◽

Cited By ~ 30

Author(s):

M.M Prieto ◽

E Montañés ◽

O Menéndez

Keyword(s):

Neural Network ◽

Power Plant ◽

Artificial Neural ◽

Performance Forecasting ◽

Early warning of some notifiable infectious diseases in China by the artificial neural network

Royal Society Open Science ◽

10.1098/rsos.191420 ◽

2020 ◽

Vol 7 (2) ◽

pp. 191420

Author(s):

Zuiyuan Guo ◽

Kevin He ◽

Dan Xiao

Keyword(s):

Neural Network ◽

Infectious Diseases ◽

Digestive Tract ◽

Early Warning ◽

Warning Signals ◽

Test Set ◽

Early Warning Signals ◽

Respiratory Infectious Diseases ◽

Artificial Neural

In order to accurately grasp the timing for the prevention and control of diseases, we established an artificial neural network model to issue early warning signals. The real-time recurrent learning (RTRL) and extended Kalman filter (EKF) methods were performed to analyse four types of respiratory infectious diseases and four types of digestive tract infectious diseases in China to comprehensively determine the epidemic intensities and whether to issue early warning signals. The numbers of new confirmed cases per month between January 2004 and December 2017 were used as the training set; the data from 2018 were used as the test set. The results of RTRL showed that the number of new confirmed cases of respiratory infectious diseases in September 2018 increased abnormally. The results of the EKF showed that the number of new confirmed cases of respiratory infectious diseases increased abnormally in January and February of 2018. The results of these two algorithms showed that the number of new confirmed cases of digestive tract infectious diseases in the test set did not have any abnormal increases. The neural network and machine learning can further enrich and develop the early warning theory.

Evolutionary Optimisation of Fully Connected Artificial Neural Network Topology

Advances in Intelligent Systems and Computing - Intelligent Computing ◽

10.1007/978-3-030-22871-2_52 ◽

2019 ◽

pp. 751-762 ◽

Cited By ~ 3

Author(s):

Jordan J. Bird ◽

Anikó Ekárt ◽

Christopher D. Buckingham ◽

Diego R. Faria

Keyword(s):

Neural Network ◽

Network Topology ◽

Evolutionary Optimisation ◽

Artificial Neural ◽

Run-off election-based decision method for the training and inference process in an artificial neural network

Scientific Reports ◽

10.1038/s41598-020-79452-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jingon Jang ◽

Seonghoon Jang ◽

Sanghyeon Choi ◽

Gunuk Wang

Keyword(s):

Neural Network ◽

Decision Rule ◽

Synaptic Weight ◽

Activation Function ◽

Network System ◽

Run Off ◽

Neural Network System ◽

Artificial Neural ◽

AbstractGenerally, the decision rule for classifying unstructured data in an artificial neural network system depends on the sequence results of an activation function determined by vector–matrix multiplication between the input bias signal and the analog synaptic weight quantity of each node in a matrix array. Although a sequence-based decision rule can efficiently extract a common feature in a large data set in a short time, it can occasionally fail to classify similar species because it does not intrinsically consider other quantitative configurations of the activation function that affect the synaptic weight update. In this work, we implemented a simple run-off election-based decision rule via an additional filter evaluation to mitigate the confusion from proximity of output activation functions, enabling the improved training and inference performance of artificial neural network system. Using the filter evaluation selected via the difference among common features of classified images, the recognition accuracy achieved for three types of shoe image data sets reached ~ 82.03%, outperforming the maximum accuracy of ~ 79.23% obtained via the sequence-based decision rule in a fully connected single layer network. This training algorithm with an independent filter can precisely supply the output class in the decision step of the fully connected network.

FORMING EVOLUTIONARY DESIGN OF NEURAL NETWORKS WITH DIFFERENT NODES

International Journal of Computing ◽

10.47839/ijc.8.1.652 ◽

2014 ◽

pp. 16-23

Author(s):

Eva Volna

Keyword(s):

Neural Network ◽

Neural Networks ◽

Pattern Recognition ◽

Feedforward Neural Network ◽

Evolutionary Design ◽

Pattern Recognition Problem ◽

Multilayer Feedforward Neural Network ◽

Artificial Neural ◽

Evolution in artificial neural networks (e.g. neuroevolution) searches through the space of behaviours for a network that performs well at a given task. Here is presented a neuroevolution system evolving populations of neurons that are combined to form the fully connected multilayer feedforward neural network with fixed architecture. In this article, the transfer function has been shown to be an important part of architecture of the artificial neural network and have significant impact on an artificial neural network’s performance. In order to test the efficiency of described method, we applied it to the pattern recognition problem and to the alphabet coding problem.

Prediction model for the risk of osteoporosis incorporating factors of disease history and living habits in physical examination of population in Chongqing, Southwest China: based on artificial neural network

BMC Public Health ◽

10.1186/s12889-021-11002-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yuqi Wang ◽

Liangxu Wang ◽

Yanli Sun ◽

Miao Wu ◽

Yingjie Ma ◽

...

Keyword(s):

Neural Network ◽

Physical Examination ◽

Prediction Model ◽

Prediction Models ◽

Support Vector ◽

Optimal Prediction ◽

Training Set ◽

Test Set ◽

Artificial Neural

Abstract Background Osteoporosis is a gradually recognized health problem with risks related to disease history and living habits. This study aims to establish the optimal prediction model by comparing the performance of four prediction models that incorporated disease history and living habits in predicting the risk of Osteoporosis in Chongqing adults. Methods We conduct a cross-sectional survey with convenience sampling in this study. We use a questionnaire From January 2019 to December 2019 to collect data on disease history and adults’ living habits who got dual-energy X-ray absorptiometry. We established the prediction models of osteoporosis in three steps. Firstly, we performed feature selection to identify risk factors related to osteoporosis. Secondly, the qualified participants were randomly divided into a training set and a test set in the ratio of 7:3. Then the prediction models of osteoporosis were established based on Artificial Neural Network (ANN), Deep Belief Network (DBN), Support Vector Machine (SVM) and combinatorial heuristic method (Genetic Algorithm - Decision Tree (GA-DT)). Finally, we compared the prediction models’ performance through accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) to select the optimal prediction model. Results The univariate logistic model found that taking calcium tablet (odds ratio [OR] = 0.431), SBP (OR = 1.010), fracture (OR = 1.796), coronary heart disease (OR = 4.299), drinking alcohol (OR = 1.835), physical exercise (OR = 0.747) and other factors were related to the risk of osteoporosis. The AUCs of the training set and test set of the prediction models based on ANN, DBN, SVM and GA-DT were 0.901, 0.762; 0.622, 0.618; 0.698, 0.627; 0.744, 0.724, respectively. After evaluating four prediction models’ performance, we selected a three-layer back propagation neural network (BPNN) with 18, 4, and 1 neuron in the input layer, hidden and output layers respectively, as the optimal prediction model. When the probability was greater than 0.330, osteoporosis would occur. Conclusions Compared with DBN, SVM and GA-DT, the established ANN model had the best prediction ability and can be used to predict the risk of osteoporosis in physical examination of the Chongqing population. The model needs to be further improved through large sample research.